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  <h1>Source code for rl_coach.exploration_policies.e_greedy</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Copyright (c) 2017 Intel Corporation </span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>

<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">RunPhase</span><span class="p">,</span> <span class="n">ActionType</span>
<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.additive_noise</span> <span class="k">import</span> <span class="n">AdditiveNoiseParameters</span>
<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.exploration_policy</span> <span class="k">import</span> <span class="n">ExplorationParameters</span><span class="p">,</span> <span class="n">ExplorationPolicy</span>
<span class="kn">from</span> <span class="nn">rl_coach.schedules</span> <span class="k">import</span> <span class="n">Schedule</span><span class="p">,</span> <span class="n">LinearSchedule</span>
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">ActionSpace</span><span class="p">,</span> <span class="n">DiscreteActionSpace</span><span class="p">,</span> <span class="n">BoxActionSpace</span>
<span class="kn">from</span> <span class="nn">rl_coach.utils</span> <span class="k">import</span> <span class="n">dynamic_import_and_instantiate_module_from_params</span>


<span class="k">class</span> <span class="nc">EGreedyParameters</span><span class="p">(</span><span class="n">ExplorationParameters</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span> <span class="o">=</span> <span class="n">LinearSchedule</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mi">50000</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="o">=</span> <span class="mf">0.05</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy_parameters</span> <span class="o">=</span> <span class="n">AdditiveNoiseParameters</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy_parameters</span><span class="o">.</span><span class="n">noise_schedule</span> <span class="o">=</span> <span class="n">LinearSchedule</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">50000</span><span class="p">)</span>
        <span class="c1"># for continuous control -</span>
        <span class="c1"># (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="s1">&#39;rl_coach.exploration_policies.e_greedy:EGreedy&#39;</span>


<div class="viewcode-block" id="EGreedy"><a class="viewcode-back" href="../../../components/exploration_policies/index.html#rl_coach.exploration_policies.e_greedy.EGreedy">[docs]</a><span class="k">class</span> <span class="nc">EGreedy</span><span class="p">(</span><span class="n">ExplorationPolicy</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    e-greedy is an exploration policy that is intended for both discrete and continuous action spaces.</span>

<span class="sd">    For discrete action spaces, it assumes that each action is assigned a value, and it selects the action with the</span>
<span class="sd">    highest value with probability 1 - epsilon. Otherwise, it selects a action sampled uniformly out of all the</span>
<span class="sd">    possible actions. The epsilon value is given by the user and can be given as a schedule.</span>
<span class="sd">    In evaluation, a different epsilon value can be specified.</span>

<span class="sd">    For continuous action spaces, it assumes that the mean action is given by the agent. With probability epsilon,</span>
<span class="sd">    it samples a random action out of the action space bounds. Otherwise, it selects the action according to a</span>
<span class="sd">    given continuous exploration policy, which is set to AdditiveNoise by default. In evaluation, the action is</span>
<span class="sd">    always selected according to the given continuous exploration policy (where its phase is set to evaluation as well).</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">action_space</span><span class="p">:</span> <span class="n">ActionSpace</span><span class="p">,</span> <span class="n">epsilon_schedule</span><span class="p">:</span> <span class="n">Schedule</span><span class="p">,</span>
                 <span class="n">evaluation_epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
                 <span class="n">continuous_exploration_policy_parameters</span><span class="p">:</span> <span class="n">ExplorationParameters</span><span class="o">=</span><span class="n">AdditiveNoiseParameters</span><span class="p">()):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        :param action_space: the action space used by the environment</span>
<span class="sd">        :param epsilon_schedule: a schedule for the epsilon values</span>
<span class="sd">        :param evaluation_epsilon: the epsilon value to use for evaluation phases</span>
<span class="sd">        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use</span>
<span class="sd">                                                         if the e-greedy is used for a continuous policy</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">action_space</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span> <span class="o">=</span> <span class="n">epsilon_schedule</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="o">=</span> <span class="n">evaluation_epsilon</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="p">,</span> <span class="n">BoxActionSpace</span><span class="p">):</span>
            <span class="c1"># for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)</span>
            <span class="n">continuous_exploration_policy_parameters</span><span class="o">.</span><span class="n">action_space</span> <span class="o">=</span> <span class="n">action_space</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy</span> <span class="o">=</span> \
                <span class="n">dynamic_import_and_instantiate_module_from_params</span><span class="p">(</span><span class="n">continuous_exploration_policy_parameters</span><span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">current_random_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">requires_action_values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">epsilon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TEST</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span><span class="o">.</span><span class="n">current_value</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_random_value</span> <span class="o">&gt;=</span> <span class="n">epsilon</span>

    <span class="k">def</span> <span class="nf">get_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">action_values</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ActionType</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="n">ActionType</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
        <span class="n">epsilon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TEST</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span><span class="o">.</span><span class="n">current_value</span>

        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="p">,</span> <span class="n">DiscreteActionSpace</span><span class="p">):</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_random_value</span> <span class="o">&lt;</span> <span class="n">epsilon</span><span class="p">:</span>
                <span class="n">chosen_action</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
                <span class="n">probabilities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">actions</span><span class="p">),</span>
                                      <span class="mf">1.</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">high</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">low</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">chosen_action</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">action_values</span><span class="p">)</span>

                <span class="c1"># one-hot probabilities vector</span>
                <span class="n">probabilities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">actions</span><span class="p">))</span>
                <span class="n">probabilities</span><span class="p">[</span><span class="n">chosen_action</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">step_epsilon</span><span class="p">()</span>
            <span class="k">return</span> <span class="n">chosen_action</span><span class="p">,</span> <span class="n">probabilities</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_random_value</span> <span class="o">&lt;</span> <span class="n">epsilon</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TRAIN</span><span class="p">:</span>
                <span class="n">chosen_action</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">chosen_action</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy</span><span class="o">.</span><span class="n">get_action</span><span class="p">(</span><span class="n">action_values</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">step_epsilon</span><span class="p">()</span>
            <span class="k">return</span> <span class="n">chosen_action</span>

    <span class="k">def</span> <span class="nf">get_control_param</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="p">,</span> <span class="n">DiscreteActionSpace</span><span class="p">):</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TEST</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span><span class="o">.</span><span class="n">current_value</span>
        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="p">,</span> <span class="n">BoxActionSpace</span><span class="p">):</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy</span><span class="o">.</span><span class="n">get_control_param</span><span class="p">()</span>

    <span class="k">def</span> <span class="nf">change_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">phase</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">change_phase</span><span class="p">(</span><span class="n">phase</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">action_space</span><span class="p">,</span> <span class="n">BoxActionSpace</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">continuous_exploration_policy</span><span class="o">.</span><span class="n">change_phase</span><span class="p">(</span><span class="n">phase</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">step_epsilon</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="c1"># step the epsilon schedule and generate a new random value for next time</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TRAIN</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">epsilon_schedule</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">current_random_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span></div>
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